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Found 1,653 Skills
Build, run, and visualize multi-step AI generation workflows. The AI architect translates natural language descriptions into connected node graphs — chain image generation, video creation, enhancement, and editing into automated pipelines.
Build and validate revenue forecasts with pipeline coverage and gap analysis. Use when forecasting revenue, validating a commit, analyzing pipeline coverage, preparing for a forecast call, doing gap analysis, calculating weighted pipeline, or reviewing deal confidence levels. Do NOT use for individual deal analysis (use /sales-deal-inspect), portfolio pipeline management (use /sales-pipeline), or building outbound cadences (use /sales-cadence).
Provides file paths to language-specific extension files for the code-testing pipeline. Call this skill to discover available extension guidance files (e.g., dotnet.md for .NET, cpp.md for C++). Do not use directly — invoked by code-testing agents and skills that need language-specific references.
This skill should be used when the user asks to "write a pipeline", "add caching", "make this build faster", "show test failures in the build page", "add annotations", "only run tests when code changes", "set up dynamic pipelines", "add retry", "parallel steps", "matrix build", "add plugins", or "work with artifacts in pipeline YAML". Also use when the user mentions .buildkite/ directory, pipeline.yml, buildkite-agent pipeline upload, step types (command, wait, block, trigger, group, input), if_changed, notify, concurrency, or asks about Buildkite CI configuration.
You are **AgentsOrchestrator**, the autonomous pipeline manager who runs complete development workflows from specification to production-ready implementation. You coordinate multiple specialist age...
Interactively prune stale non-terminal workflows from the pipeline. Use when the user says 'prune workflows', 'clean stale workflows', 'pipeline cleanup', or runs /prune. Runs a dry-run preview, displays candidates with staleness and safeguard skips, prompts the user to proceed/abort/force, then bulk-cancels approved workflows with a workflow.pruned audit event. Safeguards skip workflows with open PRs or recent commits unless force is set.
Extract text from PDFs as structured, semantic Markdown. Use when converting a PDF to Markdown, extracting text from a PDF, processing one or more PDFs into Markdown output, reading PDF contents for analysis, ingesting documents for RAG pipelines, preparing PDFs for LLM context, or any task where PDF text needs to be in a machine-readable format. ALWAYS use this skill when the user has a PDF and needs its content as text or Markdown — even if they don't explicitly say "convert to markdown".
Use when designing or modifying Elasticsearch ingest pipelines, including single-path parsing, branching logic, sub-pipelines, enrichment processors, and robust on_failure handling.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Microsoft Defender for DevOps integration with Azure Pipelines (2025)
Analyzes pipeline coverage, tracks forecast accuracy with MAPE, and calculates GTM efficiency metrics for SaaS revenue optimization
GitLab REST API via curl. Use this skill to manage projects, issues, merge requests, and pipelines in GitLab.